from __future__ import annotations

import base64
import hashlib
import json

import pytest

from crypto_spot_bot.models import Candle
from crypto_spot_bot.orderbook_features import aggregate_orderbook_observations
from crypto_spot_bot.shadow import shadow_gate_snapshot
from crypto_spot_bot.storage import Storage
from crypto_spot_bot.time_series import _feature_matrix
from crypto_spot_bot.training_coordination import TrainingCoordinator


def test_orderbook_aggregation_is_bucketed_and_rejects_sparse_hours() -> None:
    rows = [
        _observation(1_700_000_000_000, imbalance=0.6, spread=2.0, mid=100.0, micro=100.01),
        _observation(1_700_000_030_000, imbalance=0.2, spread=4.0, mid=100.0, micro=99.99),
        _observation(1_700_003_600_000, imbalance=-0.9, spread=8.0, mid=100.0, micro=100.02),
    ]

    features, manifest = aggregate_orderbook_observations(
        rows,
        interval="60",
        min_samples_per_bucket=2,
    )

    assert manifest["BTCUSDT"]["covered_buckets"] == 1
    assert manifest["BTCUSDT"]["rejected_buckets"] == 1
    values = next(iter(features["BTCUSDT"].values()))
    assert values["l1_imbalance_mean"] == pytest.approx(0.4)
    assert values["l1_imbalance_std"] == pytest.approx(0.2)
    assert values["l1_spread_bps_mean"] == pytest.approx(3.0)
    assert values["l1_microprice_deviation_bps_mean"] == pytest.approx(0.0)


def test_feature_matrix_uses_only_the_matching_closed_candle_bucket() -> None:
    candles = [
        Candle(timestamp=0, open=100, high=101, low=99, close=100, volume=1, turnover=100),
        Candle(timestamp=3_600_000, open=100, high=101, low=99, close=100, volume=1, turnover=100),
    ]
    features = {
        "BTCUSDT": {
            0: {"l1_imbalance_mean": 0.25},
            3_600_000: {"l1_imbalance_mean": -0.75},
            7_200_000: {"l1_imbalance_mean": 0.99},
        }
    }

    matrix = _feature_matrix(
        candles,
        ["l1_imbalance_mean"],
        symbol="BTCUSDT",
        orderbook_features=features,
    )

    assert matrix == [[0.25], [-0.75]]


def test_shadow_gate_uses_only_settled_forward_predictions(tmp_path, monkeypatch) -> None:
    monkeypatch.setenv("SHADOW_GATE_MIN_SETTLED", "2")
    monkeypatch.setenv("SHADOW_GATE_MIN_ELIGIBLE", "2")
    monkeypatch.setenv("SHADOW_GATE_MIN_SYMBOLS", "1")
    monkeypatch.setenv("SHADOW_GATE_MIN_DIRECTION_ACCURACY", "0.5")
    monkeypatch.setenv("SHADOW_GATE_MAX_BRIER", "0.25")
    storage = Storage(tmp_path / "bot.sqlite3")
    model_sha = "a" * 64
    for timestamp, actual in ((1, 1.0), (2, 2.0)):
        storage.insert_shadow_prediction(
            model_sha256=model_sha,
            symbol="BTCUSDT",
            forecast_timestamp_ms=timestamp,
            horizon=1,
            reference_price=100.0,
            expected_return_percent=1.0,
            probability_up=0.8,
            eligible_signal=True,
        )
        row = storage.pending_shadow_predictions(
            model_sha256=model_sha,
            symbol="BTCUSDT",
        )[0]
        storage.settle_shadow_prediction(
            row["id"],
            actual_return_percent=actual,
            take_profit_first=True,
        )

    gate = shadow_gate_snapshot(storage, model_sha)

    assert gate["state"] == "passed"
    assert gate["settled_predictions"] == 2
    assert gate["eligible_predictions"] == 2
    assert gate["total_net_percent"] == pytest.approx(3.0)


def test_shadow_bundle_does_not_replace_active_model(tmp_path) -> None:
    active = {"type": "active-model"}
    (tmp_path / "lstm_forecaster.json").write_text(json.dumps(active), encoding="utf-8")
    coordinator = TrainingCoordinator(tmp_path)
    job = coordinator.request_retrain({"source": "test"})["job"]
    lease_token = coordinator.claim({"worker_id": "worker-1"})["lease_token"]
    model = {
        "type": "pytorch_recurrent_forecaster",
        "symbols": {
            "BTCUSDT": {
                "model": "torch_gru",
                "lookback": 4,
                "input_size": 1,
                "hidden_size": 1,
                "state_dict": {"weight_ih_l0": [[0.0]]},
                "head_weight": [[0.0]],
                "head_bias": [0.0],
            }
        },
    }
    model_payload = (json.dumps(model) + "\n").encode()
    model_sha = hashlib.sha256(model_payload).hexdigest()
    artifacts = {
        "lstm_forecaster.shadow.json": model_payload,
        "torch_shadow_guard.json": (
            json.dumps({"accepted": True, "candidate_artifact_sha256": model_sha}) + "\n"
        ).encode(),
        "torch_shadow_calibration.json": (
            json.dumps(
                {
                    "artifact_sha256": model_sha,
                    "validation": {
                        "passed": True,
                        "protocol": "untouched_model_holdout_with_threshold_walk_forward",
                    },
                }
            )
            + "\n"
        ).encode(),
    }
    for name, payload in artifacts.items():
        coordinator.save_artifact_chunk(
            job["id"],
            {
                "name": name,
                "index": 0,
                "total": 1,
                "sha256": hashlib.sha256(payload).hexdigest(),
                "data_base64": base64.b64encode(payload).decode("ascii"),
                "lease_token": lease_token,
            },
        )

    completed = coordinator.complete(
        job["id"],
        {"success": True, "summary": {"accepted": True, "deployment": "shadow"}, "lease_token": lease_token},
    )

    assert completed["job"]["status"] == "completed"
    assert json.loads((tmp_path / "lstm_forecaster.json").read_text(encoding="utf-8")) == active
    assert json.loads((tmp_path / "lstm_forecaster.shadow.json").read_text(encoding="utf-8"))["type"] == "pytorch_recurrent_forecaster"


def _observation(timestamp_ms: int, *, imbalance: float, spread: float, mid: float, micro: float) -> dict:
    return {
        "symbol": "BTCUSDT",
        "bid_price": mid - 0.01,
        "bid_size": 2.0,
        "ask_price": mid + 0.01,
        "ask_size": 1.0,
        "mid_price": mid,
        "microprice": micro,
        "spread_bps": spread,
        "imbalance": imbalance,
        "source_timestamp_ms": timestamp_ms,
        "created_at": "",
    }